Unsupervised Recipe Specifications

For unsupervised processing, there is a choice of distance function to use for clustering. The options are:

Euclidean

Traditional Euclidean distance. The distance between two objects will be calculated as the square root of the sum of the squares of the difference in each dimension.

Manhattan

The distance between two objects will be calculated as the sum of the absolute value of the difference in each dimension.

One Class

A proprietary distance function that works well for highly imbalanced datasets.